Large-scale low-speed rolling element bearings form crucial connections in offshore installations such as heavy-lifting cranes, single point mooring systems, and wind turbines. Due to the stochastic nature of wind and waves, the applied loads on these bearings are hardly predictable. Furthermore, the remote nature of the offshore environment requires a high standard of operational safety and reliability, reinforcing the need for advanced inspection and monitoring methods. Acoustic emission (AE) is a continuous monitoring technique for condition assessment of low-speed bearings, as it may detect the stress waves associated with developing degradation within the rolling elements. This paper presents an investigation on the influence of grease contamination on acoustic emission (AE) monitoring of low-speed bearings. It discusses several experiments that have been conducted in various regimes of particle contaminated lubrication, and proposes a strategy for condition monitoring. The presented experiments have been conducted with differing bearing geometries in multiple testing environments. The contamination particles have been both naturally developed and artificially introduced. The results total of 9 experimental cases are discussed and compared. All experiments follow the same data-acquisition principles, utilising arrays of three AE transducers sensitive in a range between 40-580 kHz. Data is recorded while operating the bearing at low speed under load. Abrasive wear of the rolling elements and crushing of the particles are concluded to be the main sources of AE due to particle contamination. Processing consists of waveform cross-correlation clustering and feature analysis. The results suggest that hard abrasive particles make significant contribution to the AE signals, which is associated with abrasive wear of the rollers and raceways. Comparisons with the contribution of softer and finer particles are presented as well. @en

This article presents an approach to identify naturally developed damage in low-speed bearings using waveform-similarity-based clustering of acoustic emissions (AEs) under fatigue loading. The approach is motivated by the notation that each recorded AE signal from a particular damage is defined by the convolution of the source signal, transfer function of the propagation path and transfer function of the utilised sensor, and may thusly be used to identify consistent AE sources, for example due to crack growth. A sequential clustering procedure is proposed, that is based on waveform cross-correlation. The supporting theoretical background of waveform similarity, rooted in an analytical formulation of waveform propagation and transmission in complex structures, is discussed. The presented methodology is evaluated through application to AE data obtained in a low-speed run-to-failure experiment utilising a densely instrumented purpose-built linear bearing segment. The implemented sensor system comprises arrays of three types of AE transducers, that is relatively low - (40–100 kHz), mid - (95–180 kHz) and high-frequency (180–580 kHz), that are situated on both the raceways and supporting substructures of either side of the bearing. Over the course of 225,000 cycles of extension and retraction, wear has been developed. A total of about ∼2,300,000 AE signals have been recorded. Analysis of the recorded data suggests the rate of degradation increases from around 70,000 cycles onwards. Highly consistent structures of clusters indicative of a localised defect in the raceway have been identified from around 170,000 cycles onwards. These clusters are characterised by hit-rates in the range of 1–2 hits per cycle and an average similarity of 93%, they comprise about half the AE activity for the periods they have been identified for. These results highlight that the proposed cross-correlation-based clustering of AE waveforms and identification of multi-channel formations in said clusters compose a suitable methodology for assessment of damage in low-speed roller bearings.@en

Highly-loaded low-speed roller bearings form crucial connections in offshore structures, such as heavy-lifting vessels, single-point mooring systems, and wind turbines. In order to safeguard the integrity and reliability of these assets and their operations, a quantitative methodology for condition monitoring of the bearings can be of substantial value. To date, a number of assessment methods have been proposed to for this purpose, e.g. based on strain, vibration, lubrication, and acoustic emission (AE) monitoring. Despite their demonstrated potential for medium- and high-speed bearings (>600 rpm), no notable success has yet been reported in the assessment of low-speed bearings subjected to naturally-developing degradation. In this dissertation, a novel methodology for the analysis of damage-induced AE and inferring the bearing condition has been proposed. Acoustic emissions in this context are ultrasonic signals generated by the release of elastic energy in a material. In solid media, these signals propagate as stress waves and can be recorded by dedicated transducers. A mathematical framework to describe the generation, propagation, transmission, and detection of transient ultrasonic waves in complex geometries has been presented. An assessment of inter-component stress-wave transmission has been performed utilising this framework. For a representative sheave bearing, results indicate that a transmission loss in the order of 15 dB is to be expected in the amplitude of the AE waves for a single rolling contact arrangement. In conjunction with a preliminary field trial regarding the ultrasonic background noise in representative operational conditions, this evaluation has shown that it is feasible to detect damage initiated AE signals from each of the rolling elements upon field implementations. A waveform-similarity based clustering algorithm has been proposed for the identification of damage-induced AE source mechanisms. Consistency in the source mechanism is theorised to indicate gradual progressive failure, such as crack growth. Through the descriptive framework, it has been shown that high similarity of the recorded signal must be the result of high similarity in the emitted source. Additional numerical verification of this assumptions on transfer path similarity has been performed, confirming the equivalence derived from the descriptive framework. A low-speed run-to-failure test was performed with a purpose-built linear bearing segment, representative of the main bearing of a mooring turret, to assess the performance of the clustering algorithm. Intermediate and final visual inspections report the development of wear comprising erosion, surface roughening, pitting and surface initiated fatigue. In independent analysis of the recorded AE signals, several highly-consistent structures of clusters were identified over multiple measurement channels. The nose raceway could be identified as the source of these structures of clusters, which matched the observed evolution of localised damage during the inspections. Based on the source-identified AE activity, a novel quantitative indicator has been proposed to infer bearing condition. The bearing condition index (BCI) adopts a value of 1 when the bearing is in good condition. The BCI drops in value as the bearing degrades, as represented by a more significant detection of clusters of similar AE signals within the normalised period of a load cycle over a multitude of measurement frequencies. Run-to-failure experiments have been conducted to assess the proposed BCI. Intermediate and final inspections report the progressive erosion and surface roughening. Additional lubrication samples collected during these inspections contained high levels of particle contamination. A direct correlation between the AE hit-rate and the particle contamination of the lubricant was observed. Utilising progressive scaling based on cluster size, the excessive influence of lubrication contamination-induced AE signals on the BCI could be reduced, while still providing a timely warning. In review, it is concluded that the proposed methodology can effectively describe the complex generation and propagation of AE due to damage evolution in highlyloaded low-speed roller bearings. The developed clustering method has shown to effectively identify patterns and trends in the AE signals at different stages of degradation, and provide the basis for filtering out noise-related signals. The formulated BCI can subsequently provide an intuitive indication of the condition of a low-speed roller bearing in an in-situ non-intrusive manner. As such, the methodology is believed to offer promising potential to contribute to the safe and continued operation of the offshore energy infrastructure.@en

In the condition monitoring of bearings using acoustic emission (AE), the restriction to solely instrument one of the two rings is generally considered a limitation for detecting signals originating from defects on the opposing non-instrumented ring or its interface with the rollers due to the signal energy loss. This paper presents an approach to evaluate transmission in low-speed roller bearings for application in passive ultrasound monitoring. An analytical framework to describe the propagation and transmission of ultrasonic waves through the geometry and interfaces of a bearing is presented. This framework has been used to evaluate the transmission of simulated damage signals in an experiment with a static bearing. The results suggest that low- to mid-frequency signals (<200 kHz), when passing through the rollers and their interfaces from one raceway to the other, can retain enough energy to be potentially detected. An average transmission loss in the range of 10–15 dB per interface was experimentally observed. @en

This study presents an approach for the detection of evolving degradation in large-scale low-speed roller bearings by clustering of Acoustic Emission (AE) events, and its application to experimental degradation data. To acquire the latter, a purpose-built linear bearing, representative of a segment of a turret bearing, has been instrumented with multiple piezoelectric AE transducers in the frequency range between 40–580 kHz. Clustering based on cross-correlation has identified a number of significant clusters that are linked to the observed damage. The results suggest that condition monitoring based on AE waveform similarity clustering is suitable for detection and identification of degradation in a large-scale roller bearing.@en

In the present study, feasibility of implementing an acoustic emission (AE) system for condition monitoring of highly-loaded and low-speed roller bearings has been quantitatively investigated. To evaluate the transmission of damage-induced AE signals inside the bearing and their detectability on the accessible surfaces, stress wave propagation through the complex geometry and interfaces of a representative offshore bearing has been studied experimentally. The results suggest that in the investigated frequency ranges, the AE signals that pass through the bearing rollers and their interface with the inner and outer raceways can potentially remain of sufficient strength to be detected for condition assessment of these bearings.@en